DBT works with BigQuery by providing a set of features and functionality specifically for data warehousing on BigQuery.
DBT allows users to write and manage SQL code to create and transform tables in BigQuery, and it also provides an abstraction layer on top of BigQuery to manage and organize data models. DBT also provides a way to handle incremental updates, testing data quality, and version control the data models.
DBT’s approach to working with BigQuery is different from other BigQuery-related tools, such as the BigQuery web UI and the BigQuery command-line tool, in a few ways:
- Abstraction Layer: DBT provides an abstraction layer on top of BigQuery, which allows users to manage and organize data models, handle incremental updates, and test data quality. Other BigQuery-related tools, such as the BigQuery web UI and the BigQuery command-line tool, do not provide this level of abstraction.
- Focus on Data Warehousing: DBT is focused on building a data warehouse on BigQuery, whereas other BigQuery-related tools, such as the BigQuery web UI and the BigQuery command-line tool, are focused on querying and managing data in BigQuery.
- User-friendly: DBT is designed to be user-friendly for data analysts and engineers, whereas other BigQuery-related tools, such as the BigQuery web UI, can be less user-friendly for non-technical users.
- Automation: DBT provides features for automation of data pipeline, management of dependencies among data models, and testing of data quality, which other BigQuery-related tools such as the BigQuery web UI and the BigQuery command-line tool, do not provide.
DBT works with BigQuery by providing a set of features and functionality specifically for data warehousing on BigQuery. It differs from other BigQuery-related tools by providing an abstraction layer, focusing on data warehousing, being user-friendly and providing automation.
